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Fundus Disease Prediction using Public database and Hospital Fundus Images from Intravitreal injections center using machine learning.

Fundus Disease Prediction using Public database and Hospital Fundus Images from Intravitreal injections center using machine learning.

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600126676
Enrollment
Unknown
Registered
2026-06-13
Start date
2026-06-20
Completion date
Unknown
Last updated
2026-06-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetic retinopathy, retinal vein occlusion, macular edema, choroidal neovascularization, age-related macular degeneration

Interventions

Gold Standard:Expert panel diagnosis based on fundus images
Index test:AI fundus disease diagnosis model

Sponsors

Tianjin Medical University Eye Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Aged >= 18 years (including diabetic and non-diabetic patients); 2.patients receiving intravitreal injection who underwent fundus photography with EyeRoboFC;

Exclusion criteria

Exclusion criteria: 1.Images with poor quality caused by refractive media opacity, inability to cooperate with imaging examination, or previous retinal surgery.

Design outcomes

Primary

MeasureTime frame
Detection of area under the receiver operating characteristic curve (AUROC) for referrable diabetic retinopathy on EyeRoboFC images compared to ophthalmologist grading;

Secondary

MeasureTime frame
The proportion and poor quality of non-gradable images;

Countries

China

Contacts

Public ContactRen Xinjun

Tianjin Medical University Eye Hospital

zlrxjrsy@126.com+86 22 86428838

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jun 21, 2026